Using gene expression signatures to identify novel treatment strategies in gulf war illness

Abstract

Background: Gulf War Illness (GWI) is a complex multi-symptom disorder that affects up to one in three veterans of this 1991 conflict and for which no effective treatment has been found. Discovering novel treatment strategies for such a complex chronic illness is extremely expensive, carries a high probability of failure and a lengthy cycle time. Repurposing Food and Drug Administration approved drugs offers a cost-effective solution with a significantly abbreviated timeline.

Methods: Here, we explore drug re-purposing opportunities in GWI by combining systems biology and bioinformatics techniques with pharmacogenomic information to find overlapping elements in gene expression linking GWI to successfully treated diseases. Gene modules were defined based on cellular function and their activation estimated from the differential expression of each module's constituent genes. These gene modules were then cross-referenced with drug atlas and pharmacogenomic databases to identify agents currently used successfully for treatment in other diseases. To explore the clinical use of these drugs in illnesses similar to GWI we compared gene expression patterns in modules that were significantly expressed in GWI with expression patterns in those same modules in other illnesses.

Results: We found 19 functional modules with significantly altered gene expression patterns in GWI. Within these modules, 45 genes were documented drug targets. Illnesses with highly correlated gene expression patterns overlapping considerably with GWI were found in 18 of the disease conditions studied. Brain, muscular and autoimmune disorders composed the bulk of these.

Conclusion: Of the associated drugs, immunosuppressants currently used in treating rheumatoid arthritis, and hormone based therapies were identified as the best available candidates for treating GWI symptoms.

Fig. 1

GWI Affected Modules. Network illustrating modules (colored spheres) identified as differentially expressed in GWI and their corresponding gene (red spheres) associations. Edges denote gene membership within a given module consistent with previous methodologies [84]

Fig. 2

GWI Affected Drug Targetable Genes. Network illustrating PharmGKB database (8.1.2015) [19] documented gene-drug (red-blue spheres) associations for genes in GWI affected modules (overlapping colored circles) arranged according to drug families (colored bars). Gene module number scheme refers to Fig. 1. Genes presented in boldface show a significant (p ≤ 0.05) difference in GWI compared to controls with an absolute fold change greater than 1.5

Fig. 3

Matrix of squared correlation values for GWI with all human diseases (Additional file 1: Table S1) in all affected modules (Fig. 1) with values between 0 and 1 according to the color bar. Diseases are arranged in decreasing GA value and modules refer to those presented in Fig. 1 and Additional file 3: Table S2. Dashed black line represent the GA 50 % mark. Modules are clustered hierarchically using a Euclidean distance metric and average linkage to generate the hierarchical tree

Fig. 4

Summary of Results. Disease – Gene – Drug associations identified via the PharmGKB database (8.1.2015) [19]. Squared correlation values of 0.542, 0.634, 0.551 for Rheumatoid Arthritis (RA) with GWI in the Netpath (1.1.2015) [24]: Tumor Necrosis Factor (TNF) alpha, PID (2014_02_14) [33]: Validated Nuclear Estrogen Receptor Alpha Network, and PID (2014_02_14) [33]: ATF-2 Transcription Factor Network pathways, respectively